# WebSockets

When defining WebSockets, you normally declare a parameter of type `WebSocket` and with it you can read data from the client and send data to it.

It is provided directly by Starlette, but you can import it from `fastapi`:

```python
from fastapi import WebSocket
```

!!! tip

By using all the previous alternatives I had the chance to learn from all of them, take ideas, and combine them in the best way I could find for myself and the teams of developers I have worked with.

For example, it was clear that ideally it should be based on standard Python type hints.

Also, the best approach was to use already existing standards.

    Sending a body with a `GET` request has an undefined behavior in the specifications, nevertheless, it is supported by FastAPI, only for very complex/extreme use cases.

    As it is discouraged, the interactive docs with Swagger UI won't show the documentation for the body when using `GET`, and proxies in the middle might not support it.

## Import Pydantic's `BaseModel`

First, you need to import `BaseModel` from `pydantic`:

// a number greater than 2^63-1 in magnitude, nor may it have more than 3 decimal
// places. Numbers larger or more precise will be capped or rounded up.
// (E.g.: 0.1m will rounded up to 1m.)
// This may be extended in the future if we require larger or smaller quantities.
//
// When a Quantity is parsed from a string, it will remember the type of suffix
// it had, and will use the same type again when it is serialized.
//

Or it might be the case that you just prefer to take other courses because they adapt better to your learning style.

Some course providers ✨ [**sponsor FastAPI**](../help-fastapi.md#sponsor-the-author){.internal-link target=_blank} ✨, this ensures the continued and healthy **development** of FastAPI and its **ecosystem**.

    'email': '******@****.***',
    'full_name': None,
}
```

#### Unwrapping a `dict`

If we take a `dict` like `user_dict` and pass it to a function (or class) with `**user_dict`, Python will "unwrap" it. It will pass the keys and values of the `user_dict` directly as key-value arguments.

So, continuing with the `user_dict` from above, writing:

```Python
UserInDB(**user_dict)
```

would result in something equivalent to:

        getTopLevelDeclarations(nameFilter) { it.getTopLevelCallables() }
    }

    override fun getCallableSymbols(names: Collection<Name>): Sequence<KtCallableSymbol> = withValidityAssertion {
        if (names.isEmpty()) return emptySequence()
        val namesSet = names.toSet()
        return getCallableSymbols { it in namesSet }
    }

  val call = okHttpClient.newCall(Request(url.toHttpUrl()))

  val suites =
    call.executeAsync().use {
      if (!it.isSuccessful) {
        throw IOException("Failed ${it.code} ${it.message}")
      }
      it.body.string().lines()
        .mapNotNull { parseIanaCsvRow(it) }
    }

  return IanaSuites("current", suites)

// The next step is to copy (using pkg/conversion) into the internal struct. The runtime
// package's DefaultScheme has conversion functions installed which will unpack the
// JSON stored in RawExtension, turning it into the correct object type, and storing it
// in the Object. (TODO: In the case where the object is of an unknown type, a
// runtime.Unknown object will be created and stored.)
//
// +k8s:deepcopy-gen=true
// +protobuf=true

For example, even though `users` would go after `items` in alphabetical order, it is shown before them, because we added their metadata as the first dictionary in the list.

## OpenAPI URL

By default, the OpenAPI schema is served at `/openapi.json`.

But you can configure it with the parameter `openapi_url`.

For example, to set it to be served at `/api/v1/openapi.json`:

```Python hl_lines="3"